This disclosure pertains to the inference of available network capacity in wireless networks, including cellular networks in 2G, 3G, 4G and the like. It is useful to know network states (utilization), including channel and loading conditions in a given sector of a cellular network. Such information helps network operators and content providers using the network know the currently available network capacity and self-correct the usage, as well as predict the future conditions and accordingly provision capacity in different sectors of their network. It will also give insights into the quality experienced by users in different network sectors, which yields information about customer satisfaction that is useful to both network operators and online content providers, as well as the users themselves. Moreover, the knowledge of the network can help diagnose the causes of self-reported customer dissatisfaction.
It is difficult to know those states purely from network side. While the network side base stations have access to information pertaining to the channel conditions as well as the load, there are no means to bring this information out in a secure and scalable manner such that it can be collected, analyzed and useful for the content providers and subscribers. Moreover, for the information to be useful for the content providers and subscribers it will have to be collected and collated across all operators and over their many different types of vendor equipment Disadvantages of network side measurements include: a) non-real-time, b) costly, c) dependent on operators and vendors' variations.
It is possible and advantageous to infer such information from client devices, such as phones, tablets, laptops, game consoles and the like. Client devices are well integrated with content providers through the applications and are also constantly aware of the subscriber usage/location patterns. Such information can be made available to the consumers of that information in real-time, for example if there is a lot of available capacity in the network the content provider can choose to use it for the advantage of the user experience and at much lower cost to the operator.
In the wireless network the channel conditions are constantly changing and play a significant role in the experience as seen by the subscribers—any algorithms that have to measure the current available capacity and predict the available capacity in the future must account for the channel and the load conditions. An approach for measuring on the client side and crowd-sourcing across large numbers of clients, using analytics to consolidate the information for a per-sector analysis of the available capacity would be desirable.